The answer is Recall and F1-score. For imbalanced classification, especially in fraud detection where false negatives carry extreme cost, Recall directly measures the proportion of actual positive cases correctly identified, making it indispensable when missing a fraud is unacceptable. The F1-score then provides a harmonic mean of precision and recall, offering a balanced single metric that prevents a model from achieving high recall simply by predicting all cases as positive. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that accuracy becomes misleading on skewed datasets, while precision focuses on false positives, not the critical false negatives. A common trap is choosing accuracy or precision, but the exam emphasizes that when the cost of missing positives is high, Recall must be prioritized, and F1-score ensures you don’t sacrifice too much precision. Memory tip: “Recall catches the crooks, F1 keeps the balance.”
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning engineer is evaluating a binary classification model for detecting fraudulent transactions. The dataset is highly imbalanced, and the cost of false negatives (missing a fraud) is very high. Which two evaluation metrics should the engineer consider? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1-score
Recall captures the proportion of actual frauds correctly identified, directly addressing false negatives. F1-score balances precision and recall, providing a single score. Accuracy is misleading on imbalanced data, precision focuses on false positives, and mean absolute error is for regression.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
F1-score
Why this is correct
F1-score combines precision and recall, giving a balanced measure that penalizes low recall.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy is high for imbalanced data even if the model misses many frauds, so it is not suitable.
✓
Recall
Why this is correct
Recall measures the fraction of frauds detected, which is critical when missing a fraud is costly.
Related concept
Read the scenario before looking for a memorised answer.
✗
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives; it may be high even if recall is low.
✗
Mean absolute error
Why it's wrong here
MAE is a regression metric, not suitable for classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F1-score — Recall captures the proportion of actual frauds correctly identified, directly addressing false negatives. F1-score balances precision and recall, providing a single score. Accuracy is misleading on imbalanced data, precision focuses on false positives, and mean absolute error is for regression.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is building a binary classifier for credit default prediction. The dataset is highly imbalanced (98% no default). They want to maximize recall for the minority class while maintaining reasonable precision. Which metric should be optimized during hyperparameter tuning?
medium
A.AUC-ROC
✓ B.F1 score
C.Accuracy
D.Precision
Why B: F1 score balances precision and recall, making it suitable for imbalanced datasets when both metrics are important. Other options are less appropriate because accuracy is misleading due to imbalance, precision ignores recall, and AUC-ROC does not directly optimize recall at a decision threshold.
Variation 2. A data scientist has trained a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). After evaluation, the model shows an accuracy of 99%, but the recall for fraud cases is only 10%. Which metric should the data scientist prioritize to improve the model's performance for fraud detection?
medium
A.Log loss
✓ B.F1-score
C.Precision
D.Area under the ROC curve (AUC-ROC)
Why B: F1-score balances precision and recall, making it more informative than accuracy for imbalanced datasets. AUC-ROC is also used but F1 directly addresses the trade-off between false positives and false negatives. Precision alone does not capture recall, and Log loss does not directly indicate recall improvement.
Variation 3. A data scientist wants to evaluate the performance of a binary classification model. The dataset is highly imbalanced with only 5% positive class. Which metric should be used to evaluate the model?
easy
A.Accuracy
B.Mean Squared Error
C.R-squared
✓ D.F1-score
Why D: F1-score balances precision and recall, making it suitable for imbalanced datasets. Accuracy can be misleading (e.g., 95% if always predicting negative). Mean Squared Error and R-squared are for regression.
Variation 4. A data scientist is training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?
easy
A.Log loss
B.Area under the ROC curve (AUC)
✓ C.F1 score
D.Accuracy
Why C: The F1 score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where the positive class is only 1%. By tuning the F1 score, the data scientist directly balances the trade-off between maximizing recall (capturing true positives) and maintaining reasonable precision (avoiding false positives), which aligns with the stated goal.
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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
Question Discussion
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